Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 576–600 of 3073 papers

Title	Date	Tasks	Status	Score
Computational Assessment of Hyperpartisanship in News Titles	Jan 16, 2023	Active LearningLanguage Modelling	CodeCode Available	5
Compute-Efficient Active Learning	Jan 15, 2024	Active Learning	CodeCode Available	5
Constraining the Parameters of High-Dimensional Models with Active Learning	May 19, 2019	Active LearningAstronomy	CodeCode Available	5
Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels	Dec 2, 2019	Active Learning	CodeCode Available	5
Committee neural network potentials control generalization errors and enable active learning	Jun 2, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available	5
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory Exploration	Sep 28, 2023	Active LearningGaussian Processes	CodeCode Available	5
Deep Bayesian Active Learning for Accelerating Stochastic Simulation	Jun 5, 2021	Active Learning	CodeCode Available	5
Active Learning for Semantic Segmentation with Multi-class Label Query	Sep 17, 2023	Active LearningMultiple Instance Learning	CodeCode Available	5
Clinical Trial Active Learning	Jul 20, 2023	Active Learning	CodeCode Available	5
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees	Feb 3, 2024	Active Learning	CodeCode Available	5
Context Selection and Rewriting for Video-based Educational Question Generation	Apr 28, 2025	Active LearningQuestion Generation	CodeCode Available	5
CFlowNets: Continuous Control with Generative Flow Networks	Mar 4, 2023	Active Learningcontinuous-control	CodeCode Available	5
Adversarial Distillation of Bayesian Neural Network Posteriors	Jun 27, 2018	Active LearningAnomaly Detection	CodeCode Available	5
Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias	May 27, 2022	Active LearningExperimental Design	CodeCode Available	5
Active Learning for Regression Using Greedy Sampling	Aug 8, 2018	Active LearningDiversity	CodeCode Available	5
CAMAL: Optimizing LSM-trees via Active Learning	Sep 23, 2024	Active Learning	CodeCode Available	5
Building a comprehensive syntactic and semantic corpus of Chinese clinical texts	Nov 7, 2016	Active LearningPOS	CodeCode Available	5
Buy Me That Look: An Approach for Recommending Similar Fashion Products	Aug 26, 2020	Active LearningArticles	CodeCode Available	5
Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing	Feb 29, 2024	Active Learning	CodeCode Available	5
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation	Jan 29, 2024	Active LearningImage Segmentation	CodeCode Available	5
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active Learning	Jan 16, 2024	16kActive Learning	CodeCode Available	5
Class Balance Matters to Active Class-Incremental Learning	Dec 9, 2024	Active Learningclass-incremental learning	CodeCode Available	5
Bidirectional Uncertainty-Based Active Learning for Open Set Annotation	Feb 23, 2024	Active Learning	CodeCode Available	5
Black-Box Batch Active Learning for Regression	Feb 17, 2023	Active Learningregression	CodeCode Available	5
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture	May 22, 2023	Active LearningDecision Making	CodeCode Available	5

Show:10 25 50

← PrevPage 24 of 123Next →

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TypiClust	Accuracy	93.2	—	Unverified
2	PT4AL	Accuracy	93.1	—	Unverified
3	Learning loss	Accuracy	91.01	—	Unverified
4	CoreGCN	Accuracy	90.7	—	Unverified
5	Core-set	Accuracy	89.92	—	Unverified
6	Random Baseline (Resnet18)	Accuracy	88.45	—	Unverified
7	Random Baseline (VGG16)	Accuracy	85.09	—	Unverified